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Attack Analysis with a Fast Graph

TRAC-tank-vertical_logo-300x243This post is co-authored by Martin Lee, Armin Pelkmann, and Preetham Raghunanda.

Cyber security analysts tend to redundantly perform the same attack queries with different input data. Unfortunately, the search for useful meta-data correlation across proprietary and open source data sets may be laborious and time consuming with relational databases as multiple tables are joined, queried, and the results inevitably take too long to return. Enter the graph database, a fundamentally improved database technology for specific threat analysis functions. Representing information as a graph allows the discovery of associations and connection that are otherwise not immediately apparent.

Within basic security analysis, we represent domains, IP addresses, and DNS information as nodes, and represent the relationships between them as edges connecting the nodes. In the following example, domains A and B are connected through a shared name server and MX record despite being hosted on different servers. Domain C is linked to domain B through a shared host, but has no direct association with domain A.

graph_image_1 This ability to quickly identify domain-host associations brings attention to further network assets that may have been compromised, or assets that will be used in future attacks.

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Going Mobile? Get Secure.

There is no turning back from the mobile trend. With more devices comes the insatiable hunger for bandwidth. After devices are connected to the network, IT must make sure each is secured and provisioned. Creativity is needed to handle these high-density environments and enforce proper policies for mobile security, while juggling other responsibilities for the business. That complexity can be a huge headache. IT needs tools that can help make the whole process simple and fast.

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